用物理模型解释雷达图像识别，让神经网络决策更透明可信。

Deep learning technologies have significantly improved performance in the field of synthetic aperture radar (SAR) image target recognition compared to traditional methods. However, the inherent ``black box" property of deep learning models leads to a lack of transparency in decision-making processes, making them difficult to be widespread applied in practice. To tackle this issue, this study proposes a physically explainable framework for complex-valued SAR image recognition, designed based on the physical process of microwave propagation. This framework utilizes complex-valued SAR data to explore the amplitude and phase information and its intrinsic physical properties. The network architecture is fully parameterized, with all learnable parameters endowed with clear physical meanings. Experiments on both the complex-valued MSTAR dataset and a self-built Qilu-1 complex-valued dataset were conducted to validate the effectiveness of framework. The de-overlapping capability of EMWaveNet enables accurate recognition of overlapping target categories, whereas other models are nearly incapable of performing such recognition. Against 0dB forest background noise, it boasts a 20\% accuracy improvement over traditional neural networks. When targets are 60\% masked by noise, it still outperforms other models by 9\%. An end-to-end complex-valued synthetic aperture radar automatic target recognition (SAR-ATR) algorithm is constructed to perform recognition tasks in interference SAR scenarios. The results demonstrate that the proposed method possesses a strong physical decision logic, high physical explainability and robustness, as well as excellent de-aliasing capabilities. Finally, a perspective on future applications is provided.